Method and apparatus for verifying vehicle ownership from an image

ABSTRACT

Some aspects of the invention relate to a mobile apparatus including an image sensor configured to convert an optical image into an electrical signal. The optical image includes an image of a vehicle license plate. The mobile apparatus includes a license plate detector configured to process the electrical signal to recover information from the vehicle license plate image. The mobile apparatus includes an interface configured to transmit the vehicle license plate information to a remote apparatus and receive verification of vehicle ownership in response to the transmission.

CLAIM OF BENEFIT TO PRIOR APPLICATIONS

This application claims the benefit of priority from U.S. patentapplication Ser. No. 14/318,397, entitled “METHOD AND SYSTEM FORPROVIDING A VALUATION DERIVED FROM AN IMAGE,” filed on Jun. 27, 2014,U.S. patent application Ser. No. 14/455,841, entitled “METHOD AND SYSTEMFOR PROVIDING A LISTING OF VEHICLES BASED ON VEHICLE LICENSE PLATEINFORMATION FROM A SIMILAR VEHICLE,” filed on Aug. 8, 2014, and U.S.patent application Ser. No. 14/613,323 entitled “METHOD AND APPARATUSFOR RECOVERING LICENSE PLATE INFORMATION FROM AN IMAGE,” filed on Feb.3, 2015.

BACKGROUND

Field

The present disclosure relates generally to a method and apparatus fordetecting license plate information from an image of a license plate andmore specifically, detecting license plate information from an opticalimage, captured by a mobile apparatus, that includes a license plateimage and several other object images.

Background

In recent years, collecting still images of license plates has become acommon tool used by authorities to catch the drivers of vehicles thatmay engage in improper or unlawful activity. For example, lawenforcement authorities have set up stationary traffic cameras tophotograph the license plates of vehicles that may be traveling above aposted speed limit at a specific portion of a road or vehicles thatdrive through red lights. Toll booth operators also commonly use suchstationary cameras to photograph vehicles that may pass through a tollbooth without paying the required toll. However, all of these scenarioshave a common thread. The camera must be manually installed andconfigured such that it will always photograph the vehicle's licenseplate at a specific angle and when the vehicle is in a specificlocation. Any unexpected modifications, such as a shift in angle orlocation of the camera would render the camera incapable of properlycollecting license plate images.

Additionally, camera equipped mobile apparatuses (e.g., smartphones)have become increasingly prevalent in today's society. Mobileapparatuses are frequently used to capture optical images and for manyusers serve as a replacement for a simple digital camera because thecamera equipped mobile apparatus provides an image that is often as goodas those produced by simple digital cameras and can easily betransmitted (shared) over a network.

The positioning constraints put on the traffic cameras make it difficultto take images of license plates from different angles and distances andstill achieve an accurate reading. Therefore, it would be difficult toscale the same license plate image capture process performed by lawenforcement authorities to mobile apparatuses. In other words, it isdifficult to derive license plate information from an image of a licenseplate taken from a mobile image capture apparatus at a variety ofangles, distances, ambient conditions, mobile apparatus motion, and whenother object images are also in the image, which hinders a user'sability to easily gather valuable information about specific vehicleswhen engaging in a number of different vehicle related activities suchas buying and selling vehicles, insuring vehicles, and obtainingfinancing for vehicles.

SUMMARY

Several aspects of the present invention will be described more fullyhereinafter with reference to various methods and apparatuses.

Some aspects of the invention relate to a mobile apparatus including animage sensor configured to convert an optical image into an electricalsignal. The optical image includes an image of a vehicle license plate.The mobile apparatus includes a license plate detector configured toprocess the electrical signal to recover information from the vehiclelicense plate image. The mobile apparatus includes an interfaceconfigured to transmit the vehicle license plate information to a remoteapparatus and receive verification of vehicle ownership in response tothe transmission.

Other aspects of the invention relate to a mobile apparatus including animage sensor configured to convert an optical image into an electricalsignal. The optical image includes several object images. One of theobject images includes a vehicle license plate image. The mobileapparatus includes a license plate detector configured to process theelectrical signal to recover information from the vehicle license plateimage from a portion of the electrical signal corresponding to said oneof the object images. The mobile apparatus includes an interfaceconfigured to transmit the vehicle license plate information to a remoteapparatus and receive verification of vehicle ownership in response tothe transmission.

Other aspects of the invention relate to a mobile apparatus including animage sensor configured to convert an optical image into an electricalsignal. The mobile apparatus includes a display. The mobile apparatusincludes a rendering module configured to render the optical image tothe display. The mobile apparatus includes an image filter configured toapply one or more filter parameters to the electrical signal based on atleast one of color temperature of the image, ambient light, and motionof the apparatus. The mobile apparatus includes a license plate detectorconfigured to process the electrical signal to recover information fromthe vehicle license plate image. The rendering module is furtherconfigured to overlay a detection indicator on the displayed image toassist the user position of the apparatus with respect to the opticalimage in response to a signal from the image filter. The renderingmodule is further configured to provide an alert to the display when thelicense plate detector fails to recover the vehicle license plateinformation. The mobile apparatus includes an interface configured totransmit the vehicle license plate information to a remote apparatus andreceive verification of vehicle ownership in response to thetransmission.

Other aspects of the invention relate to a computer program product fora mobile apparatus having an image sensor configured to convert anoptical image into an electrical signal. The optical image includesseveral object images. One of the object images includes an image of avehicle license plate. The computer program product includes a machinereadable medium including code to process the electrical signal toselect said one of the object images. The machine readable mediumincludes code to process a portion of the electrical signalcorresponding to the selected said one of the object images to recoverinformation from the vehicle license plate image. The machine readablemedium includes code to transmit the vehicle license plate informationto a remote apparatus. The machine readable medium includes code toreceive verification of vehicle ownership in response to thetransmission.

Other aspects of the invention relate to a mobile apparatus including animage sensor configured to convert an optical image into an electricalsignal. The optical image includes an image of a vehicle license plate.The mobile apparatus includes a timing circuit configured to sample theelectrical signal at a frame rate. The mobile apparatus includes alicense plate detector configured to process the sampled electricalsignal to recover information from the vehicle license plate image. Themobile apparatus includes an interface configured to transmit thevehicle license plate information to a remote apparatus and receiveverification of vehicle ownership in response to the transmission.

It is understood that other aspects of methods and apparatuses willbecome readily apparent to those skilled in the art from the followingdetailed description, wherein various aspects of apparatuses and methodsare shown and described by way of illustration. As understood by one ofordinary skill in the art, these aspects may be implemented in other anddifferent forms and its several details are capable of modification invarious other respects. Accordingly, the drawings and detaileddescription are to be regarded as illustrative in nature and not asrestrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

Various aspects of processes and apparatuses will now be presented inthe detailed description by way of example, and not by way oflimitation, with reference to the accompanying drawings, wherein:

FIG. 1 conceptually illustrates an exemplary embodiment of an apparatusthat is capable of capturing an optical image and detecting a licenseplate image from the optical image.

FIG. 2 illustrates an exemplary embodiment transmitting license plateinformation derived from an optical image to an external server.

FIG. 3 illustrates an exemplary embodiment of an apparatus fordisplaying a vehicle ownership confirmation from a license plate image.

FIG. 4 conceptually illustrates an exemplary embodiment of a process ofverifying vehicle ownership from an optical image.

FIGS. 5a and 5b conceptually illustrate an exemplary embodiment of aprocess of verifying vehicle ownership from a video.

FIG. 6 illustrates an exemplary embodiment of a system architecture of alicense plate detection apparatus.

FIG. 7 illustrates an exemplary embodiment of a diagram of the formatconverter.

FIG. 8 illustrates an exemplary embodiment of a diagram of the imagefilter.

FIG. 9 illustrates an exemplary embodiment of a diagram of a licenseplate detector.

FIG. 10 illustrates an exemplary embodiment of an object image with aconvex hull fit around the object image.

FIG. 11 illustrates an exemplary embodiment of a method for forming aquadrilateral from a convex hull.

FIG. 12 illustrates an exemplary embodiment of an object image enclosedin a quadrilateral.

FIG. 13 illustrates an exemplary embodiment of a diagram of therendering module.

FIG. 14 illustrates an exemplary embodiment of a scene that may becaptured by a license plate detection apparatus.

FIG. 15 provides a high level illustration of an exemplary embodiment ofhow an image may be rendered on a mobile apparatus by the license platedetection apparatus and transmission of a detected license plate imageto a server.

FIG. 16 conceptually illustrates an exemplary embodiment of a moredetailed process for processing an electrical signal to recover licenseplate information.

FIG. 17 illustrates an exemplary embodiment of an object imagecomprising a license plate image within a rectangle.

FIG. 18 illustrates an exemplary embodiment of an object imagecomprising a license plate image within a quadrilateral.

FIG. 19 is an illustration of an exemplary embodiment of the dewarpingprocess being performed on a license plate image.

FIG. 20 conceptually illustrates an exemplary embodiment of a processfor processing an optical image comprising a license plate image.

FIG. 21 illustrates an exemplary embodiment of a diagram for determiningwhether a patch is an actual license plate image.

FIG. 22 conceptually illustrates an exemplary embodiment of a processfor processing a patch comprising a candidate license plate image.

FIG. 23 illustrates an exemplary embodiment of a data flow forconfirming ownership of a vehicle.

FIG. 24 illustrates an exemplary embodiment of an operating environmentfor communication between a gateway and client apparatuses.

FIG. 25 illustrates an exemplary embodiment of data flow between agateway and various other modules.

FIG. 26 conceptually illustrates an exemplary embodiment of a processfor transmitting confirmation of vehicle ownership from a license plateimage.

FIG. 27 illustrates an exemplary embodiment of an electronic system thatmay implement the license plate detection apparatus.

DETAILED DESCRIPTION

The detailed description set forth below in connection with the appendeddrawings is intended as a description of various exemplary embodimentsof the present invention and is not intended to represent the onlyembodiments in which the present invention may be practiced. Thedetailed description includes specific details for the purpose ofproviding a thorough understanding of the present invention. However, itwill be apparent to those skilled in the art that the present inventionmay be practiced without these specific details. In some instances,well-known structures and components are shown in block diagram form inorder to avoid obscuring the concepts of the present invention. Acronymsand other descriptive terminology may be used merely for convenience andclarity and are not intended to limit the scope of the invention.

The word “exemplary” or “embodiment” is used herein to mean serving asan example, instance, or illustration. Any embodiment described hereinas “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments. Likewise, the term “embodiment” ofan apparatus, method or article of manufacture does not require that allembodiments of the invention include the described components,structure, features, functionality, processes, advantages, benefits, ormodes of operation.

It will be further understood that the terms “comprises” and/or“comprising,” when used in this specification, specify the presence ofstated features, steps, operations, elements, and/or components, but donot preclude the presence or addition of one or more other features,integers, steps, operations, elements, components, and/or groupsthereof. The term “and/or” includes any and all combinations of one ormore of the associated listed items.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by aperson having ordinary skill in the art to which this invention belongs.It will be further understood that terms, such as those defined incommonly used dictionaries, should be interpreted as having a meaningthat is consistent with their meaning in the context of the relevant artand the present disclosure and will not be interpreted in an idealizedor overly formal sense unless expressly so defined herein.

In the following detailed description, various aspects of the presentinvention will be presented in the context of apparatuses and methodsfor recovering vehicle license plate information from an image. However,as those skilled in the art will appreciate, these aspects may beextended to recovering other information from an image. Accordingly, anyreference to an apparatus or method for recovering vehicle license plateinformation is intended only to illustrate the various aspects of thepresent invention, with the understanding that such aspects may have awide range of applications.

FIG. 1 conceptually illustrates an exemplary embodiment of an apparatus130 that is capable of capturing an optical image and detecting alicense plate 120 from the optical image. The apparatus 130 may be amobile phone, personal digital assistants (PDA), smart phone, laptopcomputer, palm-sized computer, tablet computer, game console, mediaplayer, digital camera, or any other suitable apparatus. FIG. 1 includesa vehicle 110, the license plate 120 registered to the vehicle 110, theapparatus 130, touch screen 140, and a user 150. The apparatus 130, ofsome embodiments, may be a wireless handheld device with built in imagecapture capabilities such as the smart phone, tablet or personal dataassistant (PDA) described above. However, in some aspects of theservice, the apparatus 130 may be a digital camera capable of processingor transferring data derived from the captured image to a personalcomputer. The information may then be uploaded from the personalcomputer to the license plate detection apparatus discussed in theforegoing.

In an exemplary embodiment of the apparatus, a customized application isinstalled on the apparatus 130. The customized application may interfacewith the apparatus' image capture device to capture an optical image,convert the optical image to an electrical signal, process theelectrical signal to detect the presence of a license plate image, andderive license plate information from a portion of the electrical signalthat is associated with the license plate image. The license plateinformation may be transmitted wirelessly to a server for furtherprocessing or decoding such as optical character recognition (OCR) ofthe license plate image. Alternatively, the OCR process may be carriedout on the mobile apparatus 130.

As shown in FIG. 1, the apparatus 130 may receive an interaction fromthe user 150 to capture an optical image that includes an object imageof the license plate 120. The interaction may occur at the touch screen140. The touch screen 140 shows an exemplary rendering of the opticalimage, including a rendering of the license plate image that may becaptured by the apparatus 130. As illustrated on the touch screen 140,the image of the license plate 120 may include a number and a state. OCRsoftware may be used to convert the state and number portions of thelicense plate image to text, which may be stored as strings to be usedlater for various functions. Once, a suitable image including a licenseplate image is captured by the apparatus 130, the license plate data maybe recovered and transmitted to a server for further processing.Additionally, the server and/or the apparatus application may provideerror checking capability to ensure that the captured image is clearenough to accurately detect and decode a license plate image. When theserver or apparatus determines that a suitable image has not beencaptured, the apparatus 130 may display an alert in the display area140, which may guide the user to acquiring a suitable image.

Alternatively, some aspects of the apparatus may provide the capabilityto bypass the image capture process to instead provide a user interfacewith text fields. For example, the user interface may provide textfields that allow for entry of the license plate number and state. Theentered information may be provided as text strings to the license platedetection apparatus without going through the detection processdiscussed above.

FIG. 2 illustrates an exemplary embodiment of transmitting license plateinformation derived from an optical image to an external server 230. Insome aspects of the apparatus, a license plate image may be transmittedafter recovering the license plate information from an image 220. Asshown, FIG. 2 includes an apparatus 210, the image 220, the server 230,and the Internet 240. The apparatus 210 may be a mobile or wirelessapparatus. The image 220 may be the same as the image rendered on thedisplay area 140 as illustrated in FIG. 1.

A license plate image recovered from the image 220 may be transmittedover the internet 240 to the server 230 where it is processed for thepurpose of detecting whether the license plate image is suitable forderiving license plate data and/or for performing OCR on the licenseplate image to derive license plate information such as the state oforigin and the license plate number.

Once the license plate image (or image file) is transmitted to theserver 230, the apparatus 210 may receive and display a confirmationmessage for confirming that the derived license plate information (e.g.,state and license plate number) is correct. In some aspects of theapparatus, the apparatus 210 may also display information about thevehicle to help the user determine whether the derived license plateinformation is correct. This may be useful in cases such as when theapparatus 210 captures a license plate image of a moving vehicle. Thevehicle license plate may no longer be in eyesight. However, it may bepossible to determine with some degree of accuracy whether the derivedlicense plate information is correct based on the vehicle informationthat is displayed on the mobile apparatus.

FIG. 3 illustrates an exemplary embodiment of an apparatus 300 fordisplaying vehicle ownership confirmation from a license plate image.The apparatus 300 may be a handheld wireless device. The apparatus 300includes a display area 310. The display area 310 includes license plateinformation 320, selectable user interface (UI) objects 330 and 335,vehicle descriptors 340, representative vehicle image 350, licenseddriver information 360, and vehicle ownership confirmation text 345.FIG. 3 illustrates two stages 301-302 of a user's interaction with theapparatus 300.

In the first stage 301, the apparatus 300 may have transmitted a licenseplate image to the server 230 for further processing. Such processingwill be described in the foregoing figures. The apparatus 300 maydisplay the license plate information 320 and the licensed driverinformation 360. The licensed driver information may be captured from asecond image of a driver's license. For instance, after receiving thelicense plate image, the apparatus may request that the user provide animage of a driver's license. The apparatus may recognize informationfrom the driver's license such as state, number, name, and address. Suchinformation may be used in order to confirm vehicle ownership.Additionally, the recognized information may be converted to text by OCRsoftware. In this exemplary illustration, the name recognized from thedriver's license may be displayed in the display area 310 as licenseddriver information 360 for verification by the user. Once the user hasverified that the information in the display area 310 matches up withthe vehicle associated with the license plate image and driver's licenseinformation, the apparatus 300 may receive a selection of the selectableUI object 335. However, in some aspects of the apparatus if thedisplayed information is not accurate, the apparatus 300 may receive aselection of the selectable UI object 330. In such aspects, theapparatus 300 may prompt the user to retake the license plate imageand/or the driver's license image. In some embodiments of the apparatus,each selectable UI object may be a selectable button that is enabledwhen a user performs a gestural interaction with the touch screen of theapparatus 300.

In the second stage 302, the apparatus 300 may have received a selectionof the selectable UI object 335. In response, the display area 310 maypresent the vehicle description 340, the representative vehicle image350, and the vehicle ownership confirmation text 345. In this exemplaryillustration, vehicle ownership is confirmed. However, in some aspectsof the apparatus, vehicle ownership may not be confirmed. In suchaspects, the vehicle ownership confirmation text 345 may display amessage indicating that the name derived from the driver's license doesnot match the registered owner of the vehicle as determined from thevehicle license plate image.

Providing the interface described in FIGS. 1-3 provides an easy andefficient way for both buyers and sellers of vehicles to make informeddecisions. For instance, if a buyer is looking to purchase a vehiclefrom a private party, the buyer is assured that the private party isactually authorized to sell the vehicle before entering into apotentially fraudulent transaction.

FIG. 4 conceptually illustrates an exemplary embodiment of a process 400of verifying vehicle ownership from an optical image. The process 400may be performed by a mobile apparatus such as the apparatus 130described with respect to FIG. 1. The process 400 may begin after animage capture capability or an application is initiated on the mobileapparatus. In some aspects of the process, the application may enablethe image capture feature on the mobile apparatus.

As shown, the process 400 captures (at 410) an optical image thatincludes a vehicle license plate image. As will be discussing in thefollowing figure, some aspects of the apparatus may process a video. Aframe may then be extracted and converted to an image file.

At 420, the process 400 converts the optical image into an electricalsignal. The process 400 then processes (at 430) the electrical signal torecover license plate information. The process 400 determines (at 440)whether the license plate information was successfully recovered. Whenthe license plate information was successfully recovered, the process400 transmits (at 460) the license plate information to a remote server.The process 400 then receives (at 470) verification of ownership for avehicle corresponding to the vehicle license plate. The process 400 thenends.

Returning to 440, when the process 400 determines that the license plateinformation was not successfully recovered, the process 400 displays (at450) an alert that the license plate information was not recovered. Insome aspects of the process, a message guiding the user to position themobile apparatus to achieve greater chances of recovering the licenseplate information may be provided with the displayed alert. The processthen ends. However, in some aspects of the process, rather than end, theprocess may optionally return to capture (at 410) another optical imageand repeat the entire process 400.

FIGS. 5a and 5b conceptually illustrate an exemplary embodiment of aprocess 500 of verifying vehicle ownership from a video. The process 500may be performed by a mobile apparatus such as the apparatus 130described with respect to FIG. 1. The process 500 may begin after animage and/or video capture capability or an application is initiated onthe mobile apparatus. The application may enable the image and/or videocapture feature on the mobile apparatus.

As shown, the process 500 converts (at 505) an optical image into anelectrical signal for sampling the electrical signal at n frames/second(fps). In some aspects of the process, the process may sample theelectrical signal at intervals such as 24 fps or any other suitableinterval for capturing video according to the apparatus' capabilities.Each sample of the electrical signal represents a frame of a video imagepresented on a display. The process 500 samples (at 510) a first portionof the electrical signal representing a first frame of the video imagepresented on the display. The process then determines (at 515) whetherany object image(s) are detected within the frame. At least one of thedetected object image(s) may comprise a license plate image. When theprocess 500 determines that at least object image exists within theframe, the process 500 assigns (at 520) a score based on the detectedobject image. The score may be based on the likelihood that at least oneof the object images is a license plate image and is discussed ingreater detail below with respect to FIGS. 21 and 22. The score may beapplied to each object image and/or aggregated for each object imagedetected in the frame. The process may then store (at 525) the score andassociated object image and frame information in a data structure. Insome aspects of the process, the process 500 may store the aggregatedobject image score and/or the process 500 may store the highest scoringobject image in the frame.

When the process 500 determines (at 515) that no object image existswithin the frame or after the process 500 stores the score (at 525), theprocess 500 displays feedback to a user based on the object imagedetected (or not detected). For instance, when no object image isdetected in the frame, the process 500 may display a message guiding theuser on how to collect a better optical image. However, when at leastone object image is detected in the frame, the process 500 may providefeedback by overlaying rectangles around the detected object image(s).Alternatively or conjunctively, the process 500 may overlay a rectanglethat provides a visual cue such as a distinct color, indicating whichobject image is determined to most likely be a license plate image orhas a higher score than other object images within the frame. In someaspects, the visual cue may be provided when a particular object imagereceives a score above a threshold value.

The process 500 optionally determines (at 535) whether user input hasbeen received to stop the video. Such user input may include a gesturalinteraction with the mobile apparatus, which deactivates the camerashutter on the mobile apparatus. When the process 500 determines (at535) that user input to stop the video capture is received, the process500 selects (at 545) the highest scoring frame according to the storedframe information. When the process 500 determines (at 535) that userinput to stop the video capture has not been received, the process 500determines (at 540) whether to sample additional portions of theelectrical signal. In some aspects of the process, such a determinationmay be based on a predetermined number of samples. For instance, themobile apparatus may have a built in and/or configurable setting for thenumber of samples to process before a best frame is selected. In otheraspects of the process, such a determination may be based on achieving ascore for a frame or object image in a frame that is above apredetermined threshold value. In such aspects, the frame or framecomprising the object image that is above the threshold score will beselected (at 545). When process 500 determines that there are moreportions of the electrical signal to be sampled, the process 500 samples(at 550) the next portion of the electrical signal representing the nextframe of the video image presented on the display. The process 500 thenreturns to detect (at 515) object image(s) within the next frame. Insome aspects of the process, the process may receive user input to stopthe video capture at any point while process 500 is running.Specifically, the process is not confined to receiving user input tohalt video capture after the feedback is displayed (at 530); the userinput may be received at anytime while the process 500 is running. Insuch aspects, if at least one object image has been scored, then theprocess 500 will still select (at 545) the highest scoring object image.However, if no object images were scored, then the process will simplyend.

In some aspects of the process, the process 500 may optionally use theobject image(s) detected in the previous sample to estimate thelocations of the object images in the sample. Using this approachoptimizes processing time when the process can determine that the mobileapparatus is relatively stable. For instance, the mobile apparatus mayconcurrently store gyro accelerometer data. The process 500 may then usegyro accelerometer data retrieved from the mobile apparatus to determinewhether the mobile apparatus has remained stable and there is a greaterlikelihood that the object image(s) will be in similar locations. Thus,when the process 500 can determine that the mobile apparatus isrelatively stable, the processing time for license plate detection maybe increased because less of the portion of the electrical signal thatrepresents the video image would need to be searched for the licenseplate image.

Alternatively or conjunctively, the process 500 may not use informationabout object image(s) from the previous frame as a predictor. Instead,the process 500 may undergo the same detection and scoring processdiscussed above. Then, for each object image that overlaps an objectimage detected in a previous frame (e.g., the object images sharesimilar pixels either by space and/or location in the frames), theprevious frame receives a higher score. Information about theoverlapping object image(s) may be maintained for optimized processinglater on. Additionally, in some aspects of the apparatus, the licenseplate detection apparatus may maintain a table of matching objectimage(s) for the sampled portions of the electrical signal representingframes of video images over time. In such aspects, some object image(s)may exist in one or a few of the frames or some may exist in many or allframes and accordingly with higher scores. In such instances, all of theoverlapping object images may be processed as discussed in greaterdetail in the foregoing sections and provided to the server for OCR oridentification. This would lead to greater accuracy in actual licenseplate detection and OCR results.

Returning now to FIGS. 5a and 5b , after selecting (at 545) the highestscoring frame, the process 500 processes (at 555) the electrical signalbased on the information associated with the selected frame to recoverlicense plate information. The process 500 then determines (at 560)whether license plate information was recovered from the electricalsignal. When the process 500 determines (at 560) that license plateinformation has been recovered, the process 500 transmits (at 570) thelicense plate information to a remote server. The process 500 thenreceives (at 575) verification of ownership for a vehicle correspondingto the vehicle license plate. The process 500 then ends.

Returning to 560, when the process 500 determines that license plateinformation was not detected, the process 500 displays (at 565) an alertthat license plate information cannot be recovered. Such an alert mayguide the user to acquiring better video that is more likely to producea readable license plate image. For instance, the alert may guide theuser's mobile device position or angle. The process 500 may then returnto collect additional video.

FIG. 6 illustrates an exemplary embodiment of a system architecture of alicense plate detection apparatus. The plate detection apparatus may bea mobile apparatus such as the apparatus 130 described with respect toFIG. 1 or any other suitable mobile apparatus that has image capture andprocessing capabilities.

The license plate detection apparatus includes an image captureapparatus 605, an imager 610, a keypad 615, a strobe circuit 685, aframe buffer 690, a format converter 620, an image filter 625, a licenseplate detector 630, a network 635, network interfaces 640 and 697, agateway 645, a rendering module 650, and a display 655. The licenseplate detection apparatus may communicate with a server having OCRModule 660, and an OCR analytics storage 670. However, in some aspectsof the apparatus, the OCR module and/or OCR analytics storage may bepart of the mobile apparatus. The license plate detection apparatusillustrated in FIG. 6 generates license plate information 675, which maybe processed by various modules communicatively coupled to the gateway.The processed information may then be used by the ownership verificationmodule 695. The ownership verification module may be used to confirmownership of a vehicle from a driver's license image and a license plateimage. In some aspects of the apparatus, the ownership verificationmodule 695 may be tied to a service a service for accessing vehicleregistration information, which is accessible via an API. In suchaspects, when a apparatus receives all the requisite criteria forconfirming ownership of the vehicle, owner verification module 695 maythen validate whether the vehicle registration information matches theinformation derived from a driver's license.

As shown, the image capture apparatus 605 communicates an optical imageto the imager 610. The image capture apparatus 605 may comprise a cameralens and/or a camera that is built into a mobile apparatus. The imager610 may comprise a CMOS array, NMOS, CCD, or any other suitable imagesensor that converts an optical image into an electrical signal (e.g.,raw image data). The electrical signal comprises pixel data associatedwith the captured image. The amount of pixel data is dependent on theresolution of the captured image. The pixel data is stored as numericalvalues associated with each pixel and the numerical values indicatecharacteristics of the pixel such as color and brightness. Thus, theelectrical signal comprises a stream of raw data describing the exactdetails of each pixel derived from the optical image. During the imagecapture process, the imager 610 may produce a digital view as seenthrough the image capture apparatus for rendering at the display 655.

In some aspects of the apparatus, the image capture apparatus 605 may beconfigured to capture video. In such aspects, a timing circuit, such asthe strobe circuit 685, may communicate with the imager 610. The strobecircuit 685 may sample (or clock) the imager 610 to produce a sampledelectrical signal at some periodicity such as 24-30 fps. The sampledelectrical signal may be representative of a frame of video presented onthe display 655. The electrical signal may be provided to the framebuffer 690. However, the imager 610 may communicate the electricalsignal directly to the format converter 620 when a single optical imageis captured. When video is captured, the frame buffer may communicatethe sample of the electrical signal representative of the frame of videofrom the frame buffer to the format converter 620. However, in someaspects of the apparatus, the frame buffer 690 may be bypassed such thatthe sampled electrical signal is communicated directly to the formatconverter 620.

The format converter 620 generates or compresses the raw image pixeldata provided in the electrical signal to a standard, space efficientimage format. However, in some aspects of the apparatus, the framebuffer 690 and format converter 620 may be reversed such that thesampled electrical signals are converted to a compressed standard videoformat before buffering. The standard image and/or video format can beread by the following modules of the license plate detection apparatus.However, the following description will assume that the sampledelectrical signals are buffered before any such format conversion. Theformat converter 620 will be described in greater detail in FIG. 7.

The format converter 620 communicates the standard image file (or image)to the image filter 625. The image filter 625 performs a variety ofoperations on the image to provide the optimal conditions to detect alicense plate image within the image. Such operations will be describedin greater detail in FIG. 8. However, if the image filter 625 determinesthat the image is too distorted, noisy, or otherwise in a condition thatis unreadable to the point that any filtering of the image will notresult in a viable image for plate detection, the image filter 625 willsignal to the rendering module 650 to display an alert on the display655 that the image is not readable. Alternatively, once the image isfiltered, ideally the image should be in a state that is optimal foraccurate license plate detection. Therefore, the image filter 625 willthen communicate the filtered image to the license plate detector 630.

The plate detector 630 is an integral module of license plate detectionapparatus. The plate detector 630 will process the image to detect thepresence of a license plate image by implementing several processeswhich will be described in greater detail in FIG. 9. The license platedetector may generate overlays such as rectangular boxes around objectimages it detects as potential or candidate license plate images. Theoverlays may be transmitted as signals from the license plate detector630 to the rendering module 650. The rendering module may instruct thedisplay 655 to display the overlays over the image received from theimager 610 so that the user of the mobile apparatus can receive visualguidance relating to what object images the license plate detectionapparatus detects as candidate license plate images. Such information isuseful in guiding the user to capture optical images that include thelicense plate image and provide a higher likelihood of accurate licenseplate information recovery.

The license plate detector 630 will determine which portion of the image(or electrical signal) is most likely a license plate image. The licenseplate detector 630 will then transmit only the license plate imageportion of the image to the network 635 by way of the network interface697. Alternatively, a user may skip the entire image conversion processand using the keypad 615, key in the license plate information, which isthen transmitted over the network 635 by way of the network interface697. The network 635 then transmits the license plate image information(or image file) or keyed information to the network interface 640, whichtransmits signals to the gateway 645.

The gateway 645 may transmit the license plate image data to the OCRmodule 660. The OCR module 660 derives the license plate informationsuch as state and number information from the license plate image. TheOCR module 660 may use several different third party and/or proprietaryOCR applications to derive the license plate information. The OCR module660 may use information retrieved from the OCR analytics storage 670 todetermine which OCR application has the greatest likelihood of accuracyin the event that different OCR applications detected differentcharacters. For instance, the OCR analytics storage 670 may maintainstatistics collected from the user input received at the apparatus 300described with respect to FIG. 3. The OCR module 660 may then select thelicense plate information that is statistically most likely to beaccurate using information retrieved from the analytics storage 670. TheOCR module 660 may then transmit the license plate information 675 as atext string or strings to the gateway 645, which provides the licenseplate information 675 to the rendering module 650 through the network635. The rendering module 650 may then instruct the display 655 todisplay the license plate information 675. The display 655 may thendisplay a message similar to the one described with respect to FIG. 3.

Additionally, the license plate information 675 may be transmittedthrough the gateway 645 and processed by various modules communicativelycoupled to the gateway 645. The gateway 645 may transmit the processedinformation to the ownership verification module 695. The ownershipverification module 695 may communicate with at least one third partyservice by way of an API to receive owner registration information basedon the license plate information. The ownership information may then beverified and a confirmation may then be transmitted back to the gateway645 for further processing. Alternatively, or in addition to, thegateway 645 may transmit the confirmation to the rendering module 650through the network 635. The rendering module 650 may then instruct thedisplay 655 to display the confirmation information along with any otherinformation to assist the user of the mobile apparatus.

In the event that the OCR module 660 or the license plate detector 630is unable to detect a license plate image or identify any license plateinformation, the OCR module 660 and/or the license plate detector 630will signal an alert to the rendering module 650, which will be renderedon the display 655.

In some aspects of the apparatus, the OCR module 660 may be located onan apparatus separate from an external server. For instance, the OCRmodule 660 may be located on the mobile apparatus 130 similar to thelicense plate detection apparatus. Additionally, in some aspects of theapparatus, the format converter 620, image filter 625, and license platedetector 630 may be located on an external server and the electricalsignal recovered from the optical image may be transmitted directly tothe network 635 for processing by the modules on the external server.

The license plate detection apparatus provide several advantages in thatit is not confined to still images. As discussed above, buffered orunbuffered video may be used by the license plate detection apparatus todetermine the frame with the highest likelihood of having a licenseplate image. It also enables optical images to be taken while a mobileapparatus is moving and accounts for object images recovered from anyangle and/or distance. Additionally, the license plate detectionapparatus also provides the added benefit of alerting the user when alicense plate image cannot be accurately detected in addition toguidance relating to how to get a better image that is more likely toproduce license plate information. Such guidance may include directionalguidance such as adjusting the viewing angle or distance as well asguidance to adjust lighting conditions, if possible. Thus, the licenseplate detection apparatus provides a solution to the complicated problemof how to derive license plate information captured from moving objectimages and from virtually any viewing angle. The license plateinformation may then be used to derive different information associatedwith the license plate information such an estimated value for avehicle.

FIG. 7 illustrates an exemplary embodiment of a diagram of the formatconverter 620. The format converter 620 receives the input of anelectrical signal that defines an image 720 or an electrical signal thatdefines a sequence of sampled images such as video frames 725. Theformat converter 620 outputs an image file 730 in a standard format suchas the formats discussed above with respect to FIG. 1. The formatconverter 620 includes a frame analyzer 715 and a conversion engine 710.When an electrical signal defining an image 720 is received at theformat converter 620, the electrical signal will be read by theconversion engine 710. The conversion engine 710 translates the pixeldata from the electrical signal into a standard, compressed image formatfile 730. Such standard formats may include .jpeg, .png, .gif, .tiff orany other suitable image format similar to those discussed with respectto FIG. 1. In the exemplary instance where the format converter 620converts video to a standard format, the standard format may include.mpeg, .mp4, .avi, or any other suitable standard video format. Sincethe electrical signal received at the format converter 620 is raw datawhich can make for a very large file, the conversion engine may compressthe raw data into a format that requires less space and is moreefficient for information recovery.

The format converter 620 may also receive a several sampled electricalsignals, each representing frames of video images, such as frame data725. The video data frames may be received at the frame analyzer 715 inthe format converter 620. The frame analyzer 715 may perform a number ofdifferent functions. For instance, the frame analyzer 715 may perform afunction of analyzing each frame and discarding any frames that areblurry, noisy, or generally bad candidates for license plate detectionbased on some detection process such as the process 500 described inFIG. 5. Those frames that are suitable candidates for license platedetection may be transmitted to the conversion engine 710 and convertedto the standard format image 730 similar to how the image 720 wasconverted.

FIG. 8 illustrates an exemplary embodiment of a diagram of the imagefilter 625. The image filter 625 receives a formatted image file thatthe image filter 625 is configured to read. The image filter 625 outputsa filtered image 840 which may be optimized for more reliable licenseplate recognition. Alternatively, if the image filter 625 determinesthat the image is unreadable, the image filter 625 may signal an alert845, indicating to the user that the image is unreadable and/or guidethe user to capture a better image.

The image filter 625 includes a filter processor 805, a grayscale filter810, and a parameters storage 835. When the image filter 625 receivesthe formatted image file 830, the filter processor 805 will retrieveparameters from the parameters storage 835, which will assist the filterprocessor 805 in how to optimally filter the image. For instance, if thereceived image was taken in cloudy conditions, the filter processor 805may adjust the white balance of the image based on the parametersretrieved from the parameters storage 835. If the image was taken in thedark, the filter processor 805 may use a de-noise function based on theparameters retrieved from the parameters storage 835 to remove excessnoise from the image. In some aspects of the apparatus, the filterprocessor 805 also has the ability to learn based on the success ofpreviously derived license plate images what parameters work best orbetter in different conditions such as those conditions described above.In such aspects, the filter processor 805 may take the learned data andupdate the parameters in the parameters storage 835 for future use.

The filter processor 805 also has logic to determine if an image will bereadable by the license plate detector 630. When the filter processor805 determines that the image will not be readable by the license platedetector 630, the filter processor 805 may signal an alert 845 to therendering module 650. However, when the filter processor 805 determinesthat sufficient filtering will generate a readable image for reliablelicense plate detection, the filter processor 805 communicates theimage, post filtering, to the grayscale filter 810.

Additionally, in some aspects of the apparatus, the image filter 625 mayreceive several images in rapid succession. Such instances may be framesof a video that may be captured while a mobile apparatus is moving. Insuch instances, the filter processor 805 may continuously adjust thefilter parameters to account for each video frame, it receives. The samealerts may be signaled in real-time in the event that a video frame isdeemed unreadable by the filter processor 805.

The grayscale filter 810 will convert the received image file tograyscale. More specifically, the grayscale filter will convert thepixel values for each pixel in the received image file 830 to new valuesthat correspond to appropriate grayscale levels. In some aspects of thefilter, the pixel values may be between 0 and 255 (e.g., 256 values or2⁸ values). In other aspects of the filter, the pixel values may bebetween 0 and any other value that is a power of 2 minus 1, such as1023, etc. The image is converted to grayscale, to simplify and/or speedup the license plate detection process. For instance, by reducing thenumber of colors in the image, which could be in the millions, to shadesof gray, the license plate image search time may be reduced.

In the grayscale image, regions with higher intensity values (e.g.,brighter regions) of the image will appear brighter than regions of theimage with lower intensity values. The grayscale filter 810 ultimatelyproduces the filtered image 840. However, one skilled in the art shouldrecognize that the ordering of the modules is not confined to the orderillustrated in FIG. 8. Rather, the image filter may first convert theimage to grayscale using the grayscale filter 810 and then filter thegrayscale image at the filter processor 805. The filter processor 805then outputs the filtered image 840. Additionally, it should be notedthat the image filter 625 and the format converter 620 may beinterchangeable. Specifically, the order in which this image isprocessed by these two modules may be swapped in some aspects of theapparatus.

FIG. 9 illustrates an exemplary embodiment of a diagram of the licenseplate detector 630. The license plate detector 630 receives a filteredimage 930 and processes the image to determine license plate information935, which is may be a cropped image of at least one license plateimage. The license plate detector 630 comprises an object detector 905,a quad processor 910, a quad filter 915, a region(s) of interestdetector 920, and a patch processor 925. The license plate detector 630provides the integral function of detecting a license plate image froman image at virtually any viewing angle and under a multitude ofconditions, and converting it to an image that can be accurately read byat least one OCR application.

The license plate detector 630 receives the filtered image 930 at theobject detector 905. As discussed above, the filtered image 930 has beenconverted to a grayscale image. The object detector 905 may use amathematical method, such as a Maximal Stable Extremal Regions (MSER)method, for detecting regions in a digital image that differ inproperties, such as brightness or color, compared to areas surroundingthose regions. Simply stated, the detected regions of the digital imagehave some properties that are constant or vary within a pre-describedrange of values; all the points (or pixels) in the region can beconsidered in some sense to be similar to each other. This method ofobject detection may provide greater accuracy in the license platedetection process than other processes such as edge and/or cornerdetection. However, in some instances, the object detector 905 may useedge and/or corner detection methods to detect object images in an imagethat could be candidate license plate images.

Typically, the object images detected by the object detector 905 willhave a uniform intensity throughout each adjacent pixel. Those adjacentpixels with a different intensity would be considered background ratherthan part of the object image. In order to determine the object imagesand background regions of the filtered image 930, the object detector905 will construct a process of applying several thresholds to theimage. Grayscale images may have intensity values between 0 and 255, 0being black and 255 being white. However, in some aspects of theapparatus, these values may be reversed with 0 being white and 255 beingblack. An initial threshold is set to be somewhere between 0 and 255.Variations in the object images are measured over a pre-determined rangeof threshold values. A delta parameter indicates through how manydifferent gray levels a region needs to be stable to be considered apotential detected object image. The object images within the image thatremain unchanged, or have little variation, over the applied deltathresholds are selected as likely candidate license plate images. Insome aspects of the detector, small variations in the object image maybe acceptable. The acceptable level of variations in an object image maybe programmatically set for successful object image detection.Conversely or conjunctively, the number of pixels (or area of the image)that must be stable for object image detection may also be defined. Forinstance, a stable region that has less than a threshold number ofpixels would not be selected as an object image, while a stable regionwith at least the threshold number of pixels would be selected as anobject image. The number of pixels may be determined based on knownvalues relating to the expected pixel size of a license plate image orany other suitable calculation such as a height to width ratio.

In addition, the object detector 905 may recognize certainpre-determined textures in an image as well as the presence ofinformative features that provide a greater likelihood that the detectedobject image may be a license plate image. Such textures may berecognized by using local binary patterns (LBP) cascade classifiers. LBPis especially useful in real-time image processing settings such as whenimages are being captured as a mobile apparatus moves around an area.Although commonly used in the art for image facial recognition, LBPcascade classifiers may be modified such that the method is optimizedfor the detection of candidate license plate images.

In an LBP cascade classification, positive samples of an object imageare created and stored on the license plate detection apparatus. Forinstance, a sample of a license plate image may be used. In someinstances multiple samples may be needed for more accurate object imagedetection considering that license plates may vary from state to stateor country to country. The apparatus will then use the sample objectimages to train the object detector 905 to recognize license plateimages based on the features and textures found in the sample objectimages. LBP cascade classifiers may be used in addition to theoperations discussed above to provide improved detection of candidatelicense plate images.

Once the object detector 905 has detected at least one object as acandidate license plate image, the object detector 905 will passinformation relating to the detected object images to the quad processor910 and/or the quad filter 915. In some aspects of the detector, theobject images may not be of a uniform shape such as a rectangle or oval.The quad processor 910 will then fit a rectangle around each detectedobject image based on the object image information provided by theobject detector 905. Rectangles are ideal due to the rectangular natureof license plates. As will be described in the foregoing, informationabout the rectangles may be used to overlay rectangles on object imagesthat are displayed for the user's view on a mobile apparatus.

The rectangle will be sized such that it fits minimally around eachobject image and all areas of the object image are within the rectanglewithout more additional background space than is necessary to fit theobject image. However, due to various factors such as the angle at whichthe optical image was taken, the license plate image may not beperfectly rectangular. Therefore, the quad processor 910 will perform aprocess on each object image using the rectangle to form a quadrilateralfrom a convex hull formed around each object image.

The quad processor 910 will use an algorithm that fits a quadrilateralas closely as possible to the detected object images in the image. Forinstance, the quad processor 910 will form a convex hull around theobject image. A convex hull is a polygon that fits around the detectedobject image as closely as possible. The convex hull comprises edges andvertices. The convex hull may have several vertices. The quad processor910 will take the convex hull and break it down to exactly four vertices(or points) that fit closely to the object image.

FIGS. 10-12 illustrate the functionality of the quad processor 910. Asshown, FIG. 10 illustrates an exemplary embodiment of an object image1005 with a convex hull 1010 fit around the object image 1005, and ablown up region 1015. The convex hull 1010 comprises several edges andvertices including vertices A-D. In order to fit the object image 1005into a quadrilateral, the convex hull 1010 may be modified such thatonly 4 vertices are used. For instance, as illustrated in FIG. 11, foreach adjacent pair of points A-D in the convex hull 1010, the quadprocessor 910 will find a new point Z that maintains convexity andenclosure of the object image when B and C are removed. Point Z ischosen as a point that provides a minimal increase to the hull area anddoes not go outside of the originally drawn rectangle (not shown). Thus,FIG. 11 illustrates an exemplary embodiment of a method for forming aquadrilateral (shown in FIG. 12) from the convex hull 1010. The processrepeats for each set of 4 points until the convex hull 1010 iscompressed to only four vertices as illustrated in FIG. 12.

FIG. 12 illustrates an exemplary embodiment of the object image 1005enclosed in a quadrilateral 1210. As shown in FIG. 12, Quadrilateral1210 fits as closely to the object image 1005 as possible without anyedge overlapping the object image 1005. Fitting a quadrilateral closelyto an object image as illustrated by the FIGS. 10-12 provides thebenefit of greater efficiency in the license plate detection process. Aswill be described below, the license plate detection apparatus will onlysearch the portions of the image within the quadrilaterals for thepresence of a license plate image.

Referring back to FIG. 9, now that the quad processor 910 has drawnefficient quadrilaterals around each of the detected object images, thecoordinates of the quadrilaterals are passed to the quad filter 915and/or the region(s) of interest detector 920. As discussed above, thelicense plate detection apparatus first overlays rectangles around eachdetected object image. The quad filter 915 may use the rectangleinformation (rather than the quadrilateral information) received fromthe object detector 905, such as the pixel coordinates of the rectanglesin the image, and look for rectangles similar in size and that overlap.The quad filter 915 will then discard the smaller rectangle(s), whilemaintaining the biggest. If at least two rectangles are of an identicalsize and overlap, the quad filter 915 will use a mechanism to determinewhich rectangle is more likely to be a full license plate image anddiscard the less likely image within the other rectangle(s). Suchmechanisms may involve textures and intensity values as determined bythe object detector 905. In some aspects of the filter, rather thansearching only rectangles, the quad filter 915 may alternatively oradditionally search the quadrilateral generated by the quad processor910 for duplicates and perform a similar discarding process. Byfiltering out the duplicates, only unique object images within therectangles will remain, with the likelihood that at least one of thoseobject images is a license plate image. Thus, at this point, the licenseplate detection apparatus will only need to search the areas within therectangles or quadrilaterals for the license plate image.

The region(s) of interest detector 920 will then determine which of theobject images are actually object images that that have similarproportions (e.g., height and width) to the proportions that would beexpected for a license plate image. For instance, typically a licenseplate is rectangular in shape. However, depending on several factorssuch as the angle that the license plate image was captured, the objectimage may appear more like a parallelogram or trapezoid. However, thereis a limit to how much skew or keystone (trapezoidal shape) a licenseplate image undergoes before it becomes unreadable. Therefore, it isnecessary to compute a skew factor and/or keystone to determine whetherthe object image may be a readable license plate image. Specifically,object images that have a skew factor and/or keystone below and/or abovea threshold value are likely object images that do not have theproportions expected for a license plate image or would likely beunreadable. Since a license plate has an expected proportion a thresholdskew factor and/or keystone may be set and any detected object imagethat has a skew factor and/or keystone indicating that the object imageis not a readable license plate image will be discarded. For instance,license plate images with a high skew and/or high keystone may bediscarded.

In some aspects of the apparatus, the skew and keystone thresholds maybe determined by digitally distorting known license plate images withvarying amounts of pitch and yaw to see where the identification processand/or OCR fails. The threshold may also be dependent on the size of theobject image or quadrilateral/trapezoid. Thus, quadrilaterals ortrapezoids must cover enough pixel space to be identified and read bythe OCR software. Those that do not have a large enough pixel space,skew factors that are too high, and/or keystones that are too high wouldthen be discarded as either being unlikely candidates for license plateimages or unreadable license plate images.

The skew factor is computed by finding the distance between opposingvertices of the quadrilateral and taking the ratio of the shorterdistance to the longer distance so that the skew factor is less than orequal to 1. Rectangles and certain parallelograms that are likelycandidate license plate images will have a skew factor that is close to0, while skewed parallelograms will have a high skew factor.Additionally, trapezoids that are likely candidate license plate imageswill have a keystone that is close to 0, while trapezoids that areunlikely candidate license plate images will have a high keystone.Therefore, object images with a high skew factor are discarded, whilethe parallelograms with a lower skew factor and trapezoids with a lowerkeystone are maintained. In some aspects of the apparatus, a thresholdskew and a threshold keystone may be defined. In such aspects,parallelograms having a skew factor below the threshold are maintainedwhile those above the threshold are discarded. Similarly, in suchaspects, trapezoids having a keystone below the threshold are maintainedwhile those above the threshold are discarded. When the value is equalto the threshold, the parallelogram or trapezoid may be maintained ordiscarded depending on the design of the apparatus.

The remaining parallelograms and trapezoids are then dewarped. Thedewarping process is particularly important for the trapezoids becauseit is used to convert the trapezoid into a rectangular image. Thedewarping process uses the four vertices of the quadrilateral and the 4vertices of an un-rotated rectangle with an aspect ratio of 2:1(width:height), or any other suitable license plate aspect ratio, tocomputer a perspective transform. The aspect ratio may be pixelwidth:pixel height of the image. The perspective transform is applied onthe region around the quadrilateral and the 2:1 aspect ratio objectimage is cropped out. The cropped object image, or patch, is an objectimage comprising a candidate license plate image.

The patch is then provided to the patch processor 925, which will searchfor alpha numeric characters in the patch, find new object images withinthe patch, fit rectangles around those object images, and compute ascore from the fit rectangles. The score may be based on a virtual linethat is drawn across the detected object images. If a line exists thathas a minimal slope, the object images on that line may receive a scorethat indicates the object image is highly likely to be a license plateimage. If no line with a minimal slope is detected, then an alert may bereturned to the rendering module that a license plate image was notdetected in the image. Scores may be calculated for several differentpatches from the same image and it follows that more than one licenseplate image may be detected in the same image. Once, the presence of alicense plate image is detected, the license plate information 935 maybe transmitted to a server for OCR and further processing. In someaspects of the apparatus, the license plate information is an image filecomprising the license plate image. Additionally, the process forscoring the patch will be described in more detail with respect to FIG.21.

FIG. 13 illustrates an exemplary embodiment of a diagram of therendering module 650. The rendering module 650 may receive as inputalert information from the image filter 1335, or information aboutdetected object images from the license plate detector 1330. Therendering module will then communicate rendering instructions 1340 tothe display 655. The rendering module 650 includes an overlay processor1305, a detection failure engine 1310, and an image renderer 1315.

The overlay processor 1305 receives information about the detectedobject images 1330 from the license plate detector 630. As discussedabove, such information may include coordinates of detected objectimages and rectangles determined to fit around those object images. Therectangle information is then provided to the detection failure engine1310, which will determine that object images have been detected by thelicense plate detector 630. The detection failure engine 1310 may thenforward the information about the rectangles to the image renderer 1315,which will provide rendering instructions 1340 to the display for howand where to display the rectangles around the image received from theimager 610. Such information my include pixel coordinates associatedwith the size and location of the rectangle and color information. Forinstance, if the license plate detector 630 determines that a detectedobject image is more likely to be an actual license plate image than theother detected object images, the rendering module 650 may instruct thedisplay 655 to display the rectangle around the more likely object imagein a way that is visually distinct from other rectangles. For instance,the rectangle around the object image more likely to be a license plateimage may be displayed in a different color than the other rectangles inthe display.

However, in some instances, the license plate detector 630 may notdetect any object images. In such instances, the overlay processor willnot forward any rectangle information to the detection failure engine1310. The detection failure engine 1310 will then determine there hasbeen an object image detection failure and signal an alert to the imagerenderer 1315. The image renderer 1315 will then communicate the displayrendering instructions 1340 for the alert to the display 655. Thelicense plate detection alerts have been described in greater detailabove.

Additionally, the image filter 625 may provide information to the imagerenderer 1315 indicating an alert that the captured image cannot beprocessed for some reason such as darkness, noise, blur, or any otherreason that may cause the image to be otherwise unreadable. The alertinformation from the image filter 625 is provided to the image renderer1315, which then provides the rendering display instructions 1340 to thedisplay 655 indicating how the alert will be displayed. The image filteralerts have been discussed in detail above.

The following FIGS. 14-22 provide exemplary illustrations and processesdetailing the functionality of the license plate detection module 630.FIGS. 14-22 are devised to illustrate how the license plate detectionapparatus goes from an optical image comprising many object images todetecting a license plate image among the object images.

FIG. 14 illustrates an exemplary embodiment of a scene 1400 that may becaptured by a mobile apparatus 1410. The mobile apparatus 1410 may besimilar to the mobile apparatus 130 described with respect to FIG. 1.The scene 1400 includes a structure 1425, a road 1430, a vehicle 1420,and a license plate 1405. The mobile apparatus 1410 includes a displayarea 1415.

As illustrated, the mobile apparatus 1410 has activated the imagecapture functionality of the mobile apparatus 1410. The image capturefunctionality may be an application that controls a camera lens andimager built into the apparatus 1410 that is capable of taking digitalimages. In some aspects of the apparatus, the image capturefunctionality may be activated by enabling an application whichactivates the license plate detection apparatus capabilities describedin FIG. 6. In this example, the mobile apparatus 1410 may be capturing astill image, several images in burst mode, or video, in real-time forprocessing by the license plate detection apparatus. For instance, thevehicle 1420 may be moving while the image capture process occurs,and/or the mobile apparatus may not be in a stationary position. In suchinstances, the license plate detection apparatus may determine the bestvideo frame taken from the video.

FIG. 15 provides a high level illustration of an exemplary embodiment ofhow an image may be rendered on an apparatus 1500 by the license platedetection apparatus and transmission of a detected license plate imageto a server 1550. As shown, the apparatus 1500 includes a display area1515 and an exploded view 1555 of the image that is rendered in displayarea 1515. The exploded view 1555 includes object images 1525,rectangles 1520 that surround the object images, overlapping rectangles1530, candidate license plate image 1505, and a rectangle 1510 thatsurrounds the candidate license plate image 1505. In the event that alicense plate image is detected and captured in the display area 1515,the apparatus 1500 may wirelessly transmit license plate image data 1535over the Internet 1540 to a server 1550 for further processing. In someaspects of the apparatus, the license plate image data may be an imagefile comprising a license plate image.

As shown in exploded view 1555, the object detector 905 of the licenseplate detection apparatus has detected several object images 1525, aswell as a candidate license plate image 1505. As shown, the renderingmodule 650 has used information communicated from the license platedetector 630 to overlay rectangles around detected object images 1525including the candidate license plate image 1505. The rendering module650 has also overlaid rectangles that differ in appearance around objectimages that are less likely to be license plate images. For instance,rectangles 1520 appear as dashed lines, while rectangle 1510 appears asa solid line. However, as those skilled in the art will appreciate, thevisual appearance of the rectangles is not limited to only thoseillustrated in exploded view 1555. In fact, the visual appearance of therectangles may differ by color, texture, thickness, or any othersuitable way of indicating to a user that at least one rectangle isoverlaid around an object image that is more likely to be a licenseplate image than the other object images in which rectangles areoverlaid.

Exploded view 1555 also illustrates overlapping rectangles 1530. Asdiscussed above, the quad filter 915 of the license plate detector 630may recognize the overlapping rectangles 1530 and discard some of therectangles, and detected object images within those discardedrectangles, as appropriate.

As is also illustrated by FIG. 15, the license plate detection apparatushas detected the presence of a candidate license plate image 1505 in theimage. As a result, the mobile apparatus 1500 will transmit the licenseplate image data 1535 associated with the license plate image over theinternet 1540 to the server 1550 for further processing. Such furtherprocessing may include OCR and using the license plate informationderived from the OCR process to perform a number of different tasks thatmay be transmitted back to the mobile apparatus 1500 for rendering onthe display area 1515. Alternatively, in some aspects of the apparatus,the OCR capability may be located on the mobile apparatus 1500 itself.In such aspects, the mobile apparatus may wirelessly transmit thelicense plate information such as state and number data rather thaninformation about the license plate image itself.

FIG. 16 conceptually illustrates an exemplary embodiment of a moredetailed process 1600 for processing an electrical signal to recoverlicense plate information as discussed at a high level in process 400.The process may also be applied to detecting license plate informationin a sample of an electrical signal representing a frame of a videoimage presented on a display as described in process 500 of FIG. 5. Theprocess 1600 may be performed by the license plate detection apparatus.The process 1600 may begin after the mobile apparatus has activated anapplication, which enables the image capture feature of a mobileapparatus.

As shown, the process 1600 converts (at 1610) the captured image tograyscale. As discussed above, converting the image to grayscale makesfor greater efficiency in distinguishing object images from backgroundaccording to the level of contrast between adjacent pixels. Severalfiltering processes may also be performed on the image during thegrayscale conversion process. The process 1600 then detects (at 1615)object image(s) from the grayscale image. Such object images may be theobject images 1505 and 1525 as illustrated in FIG. 15. The process 1600processes (at 1620) a first object image. The processing of objectimages will be described in greater detail in the foregoing description.

At 1625, the process 1600 determines whether an object image fits thecriteria for a license plate image. When the object image fits thecriteria for a license plate image, the process 1600 transmits (at 1630)the license plate image (or image data) to a server such as the server1550. In some aspects of the process, an object image fits the criteriafor a license plate when a score of the object image is above athreshold value. Such a score may be determined by a process which willbe discussed in the foregoing description. The process 1600 thendetermines (at 1635) whether there are more object images detected inthe image and/or whether the object image being processed does notexceed a threshold score.

When the process 1600 determines (at 1625) that an object image does notfit the criteria for a license plate image, the process 1600 does nottransmit any data and determines (at 1635) whether more object imageswere detected in the image and/or whether the object image beingprocessed did not exceed a threshold score. When the process 1600determines that more object images were detected in the image and/or theobject image being processed did not exceed a threshold score, theprocess 1600 processes (at 1640) the next object image. The process thenreturns to 1625 to determine if the object image fits the criteria of alicense plate image.

When the process 1600 determines (at 1635) that no more object imageswere detected in the image and/or the object image being processedexceeds a threshold score, the process 1600 determines (at 1645) whetherat least one license plate image was detected in the process 1600. Whena license plate image was detected, the process ends. When a licenseplate image was not detected, an alert is generated (at 1650) and therendering module 650 sends instructions to display a detection failuremessage at the display 655. In some aspects of the process, thedetection failure alert may provide guidance to the user for capturing abetter image. For instance, the alert may guide the user to move themobile apparatus in a particular direction such as up or down and/oradjust the tilt of the mobile apparatus. Other alerts may guide the userto find a location with better lighting or any other suitable messagethat may assist the user such that the license plate detection apparatushas a greater likelihood of detecting at least one license plate imagein an image.

The process 1600 may be performed in real-time. For instance, theprocess 1600 may be performed successively as more images are capturedeither by capturing several frames of video as the mobile apparatus orobject images in the scene move and/or are tracked or by using an imagecapture device's burst mode. The process 1600 provides the advantage ofbeing able to detect and read a license plate image in an image atvirtually any viewing angle and under a variety of ambient conditions.Additionally, the criteria for determining a license plate image isdetermined based on the operations performed by the license platedetector. These operations will be further illustrated in the followingfigures as well.

FIGS. 17-19 illustrate the operations performed by the quad processor910. For instance, FIG. 17 illustrates an exemplary embodiment of animage 1700 comprising a license plate image 1710. Although not shown,the license plate may be affixed to a vehicle which would also be partof the image. The image 1700 includes the license plate image 1710 and arectangle 1705. As shown in exemplary image portion 1700, an objectimage has been detected by the object detector 905 of the license platedetector 630. The object image in this example is the license plateimage 1710. After the object image was detected, the quad processor 910fit a rectangle 1705 around the license plate image 1710. Informationassociated with the rectangle may be provided to the rendering module650 for overlaying a rectangle around the detected license plate imagein the image displayed on the display 655.

FIG. 18 illustrates an exemplary embodiment of a portion of an image1800 comprising the same license plate image 1710 illustrated in FIG.17. Image portion 1800 includes the license plate image 1810 and aquadrilateral 1805. As discussed above with respect to FIGS. 10-12, thequad processor 910 of the license plate detector 630 performs severalfunctions to derive a quadrilateral that closely fits the detectedobject image. Once the quadrilateral has been derived, the quadprocessor 910 then computes the skew factor and/or keystone discussedabove.

Once the quadrilateral is determined to have a low skew (or a skew belowa threshold value) or the trapezoid has been determined to have a lowkeystone (or a keystone below a threshold value), the region(s) ofinterest detector 920 can dewarp the image to move one step closer toconfirming the presence of a license plate image in the image and toalso generate patch that is easily read by OCR software. In some aspectsof the apparatus, the patch is the license plate image that has beencropped out of the image.

FIG. 19 is an exemplary embodiment of the dewarping process beingperformed on a license plate image 1905 to arrive at license plate image1910. FIG. 19 illustrates two stages 1901 and 1902 of the dewarpingprocess.

As shown, the first stage 1901 illustrates the license plate image 1905in a trapezoidal shape similar to the shape of the quadrilateral 1805illustrated in FIG. 18. The second stage 1902 illustrates the licenseplate image 1910 after the dewarping process has been performed. Asshown, license plate image 1910 has undergone a perspective transformand rotation. The license plate image 1910 as shown in the second stage1902 is now in a readable rectangular shape. In some aspects of thedewarping process, the license plate image may also undergo correctionsif the license plate image is skewed or may scale the license plateimage to a suitable size.

The ability to accurately dewarp quadrilaterals and especially thequadrilaterals that are license plate images taken at any angle is anintegral piece of the license plate detection apparatus. The dewarpingcapability enables a user to capture an image of a license plate at avariety of different angles and distances. For instance, the image maybe taken with any mobile apparatus at virtually any height, direction,and/or distance. Additionally, it provides the added benefit of beingable to capture a moving image from any position. Once the license plateimage has been dewarped, the region(s) of interest detector 920 willcrop the rectangular license plate image to generate a patch. The patchwill be used for further confirmation that the license plate image 1910is, in fact, an image of a license plate.

FIG. 20 conceptually illustrates an exemplary embodiment of a process2000 for processing an image comprising a license plate image. Theprocess 2000 may be performed by a license plate detection apparatus.The process 2000 may start after the license plate detection apparatushas instantiated an application that enables the image capture featureof a mobile apparatus.

As shown, the process 2000 detects (at 2010) at least one object image,similar to the object image detection performed by process 1600. Thefollowing describes in greater detail the process of processing (at1620) the image.

For instance, the process 2000 then fits (at 2015) a rectangle to eachdetected object image in order to reduce the search space to thedetected object images. The information associated with the rectanglemay also be used as an overlay to indicate to users of the license platedetection apparatus the location(s) of the detected object image(s). Theprocess then uses the rectangles to form (at 2020) a convex hull aroundeach object image. The convex hull, as discussed above, is a polygon ofseveral vertices and edges that fits closely around an object imagewithout having any edges that overlap the object image.

At 2025, the process 2000 compresses the convex hull to a quadrilateralthat closely fits around the detected object image. The process ofcompressing the convex hull into a quadrilateral was discussed in detailwith respect to FIGS. 9-12. The process 2000 then filters (at 2030)duplicate rectangles and/or quadrilaterals. In some aspects of theprocess, rectangles or quadrilaterals that are similar in size andoverlap may be discarded based on some set criteria. For example, thesmaller rectangle and/or quadrilateral may be discarded.

The process 2000 calculates (at 2035) a skew factor. The process 2000then dewarps (at 2040) the quadrilateral. The process then crops (at2045) the object image within the quadrilateral, which becomes thepatch. The patch will be used for further processing as discussed below.In some aspects of the process, the object image is cropped at aparticular ratio that is common for license plates of a particularregion or type. For instance, the process may crop out a 2:1 aspectratio patch, of the image, which is likely to contain the license plateimage. Once the quadrilateral is cropped, the process 2000 then ends.

FIG. 21 illustrates an exemplary embodiment of a diagram that determineswhether a patch 2100 is an actual license plate image. The patch 2100includes a candidate license plate image 2105, alpha-numeric characters2120 and 2140, rectangles 2115, sloped lines 2130, zero-slope line 2110,and graphic 2125.

As shown in the patch 2100, rectangles are fit around detected objectimages within the patch. In some aspects of the apparatus, object imagesmay be detected using the MSER object detection method. Conjunctively orconversely, some aspects of the apparatus, may use edge and or cornerdetection methods to detect the object images. In this case, thedetected object images are alpha-numeric characters 2120 and 2140 aswell as graphic 2125. After detecting the alpha-numeric characters 2120and 2140 as well as graphic 2125, a stroke width transform (SWT) may beperformed to partition the detected object images into those that arelikely from an alpha-numeric character and those that are not. Forinstance, the SWT may try to capture the only alpha-numeric effectivefeatures and use certain geometric signatures of alpha-numericcharacters to filter out non-alpha-numeric areas, resulting in morereliable text regions. In such instances, the SWT transform maypartition the alphanumeric characters 2120 and 2140 from the graphic2125. Thus, only those object images that are determined to likely bealpha-numeric characters, such as alphanumeric characters 2120 and 2140,are later used in a scoring process to be discussed below. In someaspects of the apparatus, some object images other than alpha-numericcharacters may pass through the SWT partitioning. Thus, furtherprocessing may be necessary to filter out the object images that are notalpha-numeric characters and also to determine whether the alpha-numericcharacters in the license plate image fit the characteristics common fora license plate images.

Following the partitioning of alpha-numeric characters from non-alphanumeric characters, a line is fit to the center of the rectangle pairfor each pair of rectangles. For instance, a sloped line is shown forthe D and object image 2125 pair. The distance of all other rectanglesto the lines 2130 and 2110 are accumulated and the pair with thesmallest summed distance is used as a text baseline. For instance, thezero-slope line 2110 has the smallest summed distance of the rectanglesto the line 2110. Some aspects of the apparatus may implement a scoringprocess to determine the presence of a license plate image. Forinstance, some aspects of the scoring process may determine a score forthe determined alpha-numeric characters on the zero-slope line 2110. Thescore may increase when the rectangle around the alpha-numeric characteris not rotated beyond a threshold amount. The score may decrease if thedetected alpha-numeric character is too solid. In some aspects of thescoring process, solidity may be defined as the character area/rectanglearea. When the calculated area is over a threshold amount, then thedetected object image may be deemed too solid and the score decreases.

In other aspects of the scoring process, for each rectangle 2115 in thepatch 2100 the patch score increases by some scoring value if the centerof the rectangle is within a particular distance of the baseline linewhere X is the shorter of the rectangle height and width. For instance,if the particular distance were to be defined as the shorter of therectangle height and width and if the scoring value is set at 1, thepatch score value of the patch 2100 would be 7 because the rectanglesaround the characters “1DDQ976” are within a shorter distance than thewidth of the rectangle. Furthermore, the zero-slope of the line 2110between the alpha-numeric characters 2120 further confirm that thispatch is likely a license plate image since typically license plateshave a string of characters along a same line. Sloped lines 2130 are,therefore, unlikely to provide any indication that the patch is alicense plate image because the distance between characters is too greatand the slope is indicative of a low likelihood of a license plateimage. Accordingly, in some aspects of the process, sloped lines 2130are discarded in the process.

In some aspects of the process, when the patch has a score above athreshold value, the patch is determined to be a license plate image,and the license plate detection is complete. The license plate imagedata is then transmitted to a server for further processing and for usein other functions computed by the server, the results of which areprovided to the license plate detection apparatus.

FIG. 22 conceptually illustrates an exemplary embodiment of a process2200 for processing a patch comprising a candidate license plate imagesuch as patch 2100. The process may be performed by the license platedetection apparatus. The process may begin after a patch has beencropped from an image file.

As shown, the process 2200 processes (at 2205) only substantiallyrectangular portion(s) of the patch to locate alpha-numeric characters.The process 2200 fits (at 2210) rectangles around the locatedalpha-numeric characters and computes scores based on the distancesbetween rectangle pairs as discussed above with respect to FIG. 21. Theprocess 2200 selects (at 2215) the patch with the best score torecognize as a license plate image. Alternatively or conjunctively, theprocess 2200 may select all patches that have a score above a thresholdlevel to be deemed as license plate images. In such instances, multiplepatches, or instances of license plate information, would be transmittedto the server for further processing.

FIG. 23 illustrates an exemplary embodiment of a data flow 2300 forconfirming ownership of a vehicle. The data flow includes license plateimage 2305, VIN 2310, vehicle registration 2315, ownership confirmation2345, a driver's license image 2320, driver's license information 2335,validation module 2330, and alert 2340.

As shown, the data flow begins with the license plate image 2305. TheVIN 2310 is the recovered from the license plate image 2305. Then thevehicle registration information 2315 is recovered from the VIN 2310.The vehicle registration information may include at least a first andlast name of the registered owner of the vehicle associated with thelicense plate image 2305.

Additionally, information from the driver's license image 2320concurrently or consecutively processed by the apparatus. The apparatusmay prompt a user to capture a driver's license image 2320 before orafter capturing the license plate image. The driver's licenseinformation 2335 is the recovered from the driver's license image 2320.Such information may be recovered by using OCR software similar to theOCR software used to recover the license plate information. Therecovered driver's license information 2335 may include information suchas state, number, name, address, date of birth and any other pertinentinformation that may assist in confirming vehicle ownership. Thedriver's license information 2335 is then transmitted to the validationmodule 2330. The validation module determines whether the driver'slicense information matches the owner information associated with theVIN. For instance, the validation module 2330 determines whether thename of the registered owner 2315 of the vehicle matched the namerecovered from the license plate image 2320. If the information does notmatch, the validation module 2330 transmits the alert 2340. Such analert may be displayed at a mobile apparatus, such as the mobileapparatus 300 described with respect to FIG. 3. However, if thevalidation module determines that the driver's license information andvehicle ownership information match, then validation module 2330 returnsa confirmation that the driver's license name matches the name of theregistered owner of the vehicle. Thus the described data flow 2300provides a simple and efficient way to assure the buyer that the privateparty selling a vehicle is authorized to actually sell the vehicle. Suchinformation may be used for other processes such as underwriting avehicle for insurance and providing vehicle refinancing from a mobileapparatus.

FIG. 24 illustrates an exemplary embodiment of an operating environment2400 for communication between a gateway 2495 and client apparatuses2410, 2430, and 2470. In some aspects of the service, client apparatuses2410, 2430, and 2470 communicate over one or more wired or wirelessnetworks 2440 and 2460. For example, wireless network 2440, such as acellular network, can communicate with a wide area network (WAN) 2480,such as the internet, by use of mobile network gateway 2450. A mobilenetwork gateway in some aspects of the service provides a packetoriented mobile data service or other mobile data service allowingwireless networks to transmit data to other networks, such as the WAN2480. Likewise, access device 2460 (e.g., IEEE 802.11b/g/n wirelessaccess device) provides communication access to the WAN 2480. Theapparatuses 2410, 2430, and 2470 can be any portable electroniccomputing apparatus capable of communicating with the gateway 2495. Forinstance, the apparatuses 2410 and 2470 may have an installedapplication that is configured to communicate with the gateway 2495. Theapparatus 2430 may communicate with the gateway 2495 through a websitehaving a particular URL. Alternatively, the apparatus 2430 may be anon-portable apparatus capable of accessing the internet through a webbrowser.

In order to process the license plate information to confirm vehicleownership, the gateway 2495 may also communicate with third partyservices 2490 that provide information such as vehicle registrationinformation and vehicle identification numbers (VIN)s. As shown, thegateway 2495 may communicate directly with at least one third partyprocessing service 2490 if such services are located on the same networkas the gateway 2495. Alternatively, the gateway 2495 may communicatewith at least one of the third party processing services 2490 over theWAN 2480 (e.g., the internet).

In some aspects of the service, the process for confirming vehicleownership may incorporate the location with the vehicle and/orapparatus. In such aspects, the service may optionally use locationinformation acquired through a GPS satellite 2420. The apparatuses 2410and 2470 may be configured to use a GPS service and provide locationinformation to the gateway 2495 using the connections discussed above.

FIG. 25 illustrates an exemplary flow of data between a gateway 2500 andvarious other modules. The gateway 2500 and modules 2510-2560 may belocated on a server such as the server 230. In some aspects of theapparatus, the gateway 2500 may be a request router in that it receivesrequests from the various modules 2510-2560 and routes the requests toat least one of the appropriate module 2510-2560. The gateway 2500communicates with various modules 2510-2560, which may communicate withvarious third party services to retrieve data that enables the gateway2500 to provide ownership verification to a client apparatus 2570 froman image of a license plate.

As shown, the client apparatus 2570, may use a network interface 2560 totransmit at least one license plate image recovered from an opticalimage taken by the client apparatus 2570. The client apparatus 2570 mayinclude an installed application providing instructions for how tocommunicate with the gateway 2500 through the network interface 2560. Inthis example, the network interface 2560 provides license plate imageinformation or text input of a license plate to the gateway 2500. Forinstance, as discussed above, the network interface 2560 may transmittext strings received as user input at the client apparatus 2570 or alicense plate image processed by the client apparatus 2570 to thegateway 2500. As further shown in this example, the gateway 2500 mayroute the license plate image data to the OCR module 2510 to perform theOCR text extraction of the license plate information. In this example,the OCR module 2510 may have specialized or a commercial OCR softwareapplication installed that enables accurate extraction of the licenseplate number and state. The OCR module may be similar to the OCR modulediscussed in FIG. 6. In one example, the OCR module 2510 may also havethe capability of determining if a license plate image contains a clearenough image that will provide for accurate text extraction. In thisexample, if the license plate image does not contain a clear image orthe image quality is too low, the OCR module may alert the gateway 2500to transmit a warning to the client apparatus 2570. In an alternativeexample, a license plate image may be recovered and transmitted to thegateway 2500 for further processing.

Once the license plate number and state information is extracted andconverted to text strings, the gateway 2500 will provide the extractedtext to a translator 2520, which is capable of determining a VIN fromthe license plate information. The translator 2520 may communicate withthird party services using functionality provided in an applicationprogramming interface (API) associated with the third party services.Such services may derive VINs from license plate information to retrievethe vehicle's VIN. In some aspects of the server, the various modules2510-2550 may also be configured to communicate with the third partyservices or apparatuses (not shown) using APIs associated with the thirdparty services. In such aspects of the server, each module 2510-2550 mayroute a request through the gateway 2500 to the network interface 2560,which will communicate with the appropriate third party service (notshown).

The gateway 2500 then routes the retrieved VIN to the VIN decoder 2530along with a request to recover the registered owner of the vehicleassociated with the license plate information. The VIN decoder 2530 iscapable of using the VIN to recovered owner registration by requestingat least such information from a third party service. Similar to the VINtranslator 2520, the VIN decoder 2530 may communicate with a third partyservice by using an API associated with the service. The registeredowner information may be routed back through the gateway 2500 andthrough the network interface 2560 to the client apparatus 2570.Although not shown, the apparatus 300 of FIG. 3 may optionally displaythe registered owner of the vehicle.

Next, the registered owner information may be provided by the gateway2500 to the ownership verification module 2550. The ownershipverification module 2550 may receive and confirm that the registeredowner information matches the name derived from an image of driver'slicense captured at the device 2570. The ownership verification module2540 may query a third party service to verify that the driver's licenseinformation matches information related to the ownership of theparticular VIN, making the VIN decoder 2530 unnecessary. Alternatively,the ownership verification module 2540 may simply perform a comparisonof the registered owner information and the driver's licenseinformation. If such information does not match up, then an alert may begenerated for the display at the apparatus 2570. However, if theownership verification module 2560 determines that the VIN and driver'slicense information match up, then the ownership verification module2560 may transmit a confirmation of ownership through the gateway 2500to the network interface 2560 so that the apparatus 2570 may display theconfirmation.

In some aspects of the apparatus, the ownership verification module 2540may compare a number of different factors to confirm ownership such asaddress, driver's license number, and any other recoverable informationfrom the vehicle registration information and driver's license image.

FIG. 26 conceptually illustrates an exemplary embodiment of a process2600 for transmitting confirmation of vehicle ownership from a licenseplate image. The process 2600 may be performed by a server such as theserver 230. The process may begin after a mobile apparatus has recovereda suitable license plate image for transmission to the server 230 and/ora driver's license image.

As shown, the process 2600 receives (at 2610) license plate imageinformation or text input from a mobile apparatus. The text input may beinformation associated with a vehicle license plate such as a state andalpha-numeric characters. The process 2600 then requests (at 2630) a VINassociated with the license plate information. The process 2600 mayrequest the VIN by sending the request to a third party server. In someaspects of the server, the process 2600 communicates with the thirdparty server by using an API.

At 2640, the process 2600 receives the VIN associated with the licenseplate information. The process 2600 then requests (at 2650) vehicleownership information using the received VIN. Such registrationinformation may include at least one of a first and last name, fullname, address, and driver's license number. The process 2600 receives(at 2660) the vehicle ownership information.

At 2680, the process 2600 receives information from a driver's licenseimage from the mobile apparatus. the process 2600 then requests (at2685) validation that the driver's license information matches thevehicle owner's information. At 2686, the process determines if theinformation matches. When the information does not match, the process2600 transmits (at 2687) an alert to the mobile apparatus indicatingthat the registration information does not match the driver's licenseinformation. When the information does match, the process 2600 transmits(at 2695) confirmation of vehicle ownership to the apparatus. Then theprocess ends.

FIG. 27 illustrates an exemplary embodiment of a system 2700 that mayimplement the license plate detection apparatus. The electronic system2700 of some embodiments may be a mobile apparatus. The electronicsystem includes various types of machine readable media and interfaces.The electronic system includes a bus 2705, processor(s) 2710, read onlymemory (ROM) 2715, input device(s) 2720, random access memory (RAM)2725, output device(s) 2730, a network component 2735, and a permanentstorage device 2740.

The bus 2705 communicatively connects the internal devices and/orcomponents of the electronic system. For instance, the bus 2705communicatively connects the processor(s) 2710 with the ROM 2715, theRAM 2725, and the permanent storage 2740. The processor(s) 2710 retrieveinstructions from the memory units to execute processes of theinvention.

The processor(s) 2710 may be implemented with one or moregeneral-purpose and/or special-purpose processors. Examples includemicroprocessors, microcontrollers, DSP processors, and other circuitrythat can execute software. Alternatively, or in addition to the one ormore general-purpose and/or special-purpose processors, the processormay be implemented with dedicated hardware such as, by way of example,one or more FPGAs (Field Programmable Gate Array), PLDs (ProgrammableLogic Device), controllers, state machines, gated logic, discretehardware components, or any other suitable circuitry, or any combinationof circuits.

Many of the above-described features and applications are implemented assoftware processes of a computer programming product. The processes arespecified as a set of instructions recorded on a machine readablestorage medium (also referred to as machine readable medium). When theseinstructions are executed by one or more of the processor(s) 2710, theycause the processor(s) 2710 to perform the actions indicated in theinstructions.

Furthermore, software shall be construed broadly to mean instructions,data, or any combination thereof, whether referred to as software,firmware, middleware, microcode, hardware description language, orotherwise. The software may be stored or transmitted over as one or moreinstructions or code on a machine-readable medium. Machine-readablemedia include both computer storage media and communication mediaincluding any medium that facilitates transfer of a computer programfrom one place to another. A storage medium may be any available mediumthat can be accessed by the processor(s) 2710. By way of example, andnot limitation, such machine-readable media can comprise RAM, ROM,EEPROM, CD-ROM or other optical disk storage, magnetic disk storage orother magnetic storage devices, or any other medium that can be used tocarry or store desired program code in the form of instructions or datastructures and that can be accessed by a processor. Also, any connectionis properly termed a machine-readable medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared (IR),radio, and microwave, then the coaxial cable, fiber optic cable, twistedpair, DSL, or wireless technologies such as infrared, radio, andmicrowave are included in the definition of medium. Disk and disc, asused herein, include compact disc (CD), laser disc, optical disc,digital versatile disc (DVD), floppy disk, and Blu-Ray® disc where disksusually reproduce data magnetically, while discs reproduce dataoptically with lasers. Thus, in some aspects machine-readable media maycomprise non-transitory machine-readable media (e.g., tangible media).In addition, for other aspects machine-readable media may comprisetransitory machine-readable media (e.g., a signal). Combinations of theabove should also be included within the scope of machine-readablemedia.

Also, in some embodiments, multiple software inventions can beimplemented as sub-parts of a larger program while remaining distinctsoftware inventions. In some embodiments, multiple software inventionscan also be implemented as separate programs. Any combination ofseparate programs that together implement a software invention describedhere is within the scope of the invention. In some embodiments, thesoftware programs, when installed to operate on one or more electronicsystems 2700, define one or more specific machine implementations thatexecute and perform the operations of the software programs.

The ROM 2715 stores static instructions needed by the processor(s) 2710and other components of the electronic system. The ROM may store theinstructions necessary for the processor(s) 2710 to execute theprocesses provided by the license plate detection apparatus. Thepermanent storage 2740 is a non-volatile memory that stores instructionsand data when the electronic system 2700 is on or off. The permanentstorage 2740 is a read/write memory device, such as a hard disk or aflash drive. Storage media may be any available media that can beaccessed by a computer. By way of example, the ROM could also be EEPROM,CD-ROM or other optical disk storage, magnetic disk storage or othermagnetic storage devices, or any other medium that can be used to carryor store desired program code in the form of instructions or datastructures and that can be accessed by a computer.

The RAM 2725 is a volatile read/write memory. The RAM 2725 storesinstructions needed by the processor(s) 2710 at runtime, the RAM 2725may also store the real-time video images acquired during the licenseplate detection process. The bus 2705 also connects input and outputdevices 2720 and 2730. The input devices enable the user to communicateinformation and select commands to the electronic system. The inputdevices 2720 may be a keypad, image capture apparatus, or a touch screendisplay capable of receiving touch interactions. The output device(s)2730 display images generated by the electronic system. The outputdevices may include printers or display devices such as monitors.

The bus 2705 also couples the electronic system to a network 2735. Theelectronic system may be part of a local area network (LAN), a wide areanetwork (WAN), the Internet, or an Intranet by using a networkinterface. The electronic system may also be a mobile apparatus that isconnected to a mobile data network supplied by a wireless carrier. Suchnetworks may include 3G, HSPA, EVDO, and/or LTE.

It is understood that the specific order or hierarchy of steps in theprocesses disclosed is an illustration of exemplary approaches. Basedupon design preferences, it is understood that the specific order orhierarchy of steps in the processes may be rearranged. Further, somesteps may be combined or omitted. The accompanying method claims presentelements of the various steps in a sample order, and are not meant to belimited to the specific order or hierarchy presented.

The various aspects of this disclosure are provided to enable one ofordinary skill in the art to practice the present invention. Variousmodifications to exemplary embodiments presented throughout thisdisclosure will be readily apparent to those skilled in the art, and theconcepts disclosed herein may be extended to other apparatuses, devices,or processes. Thus, the claims are not intended to be limited to thevarious aspects of this disclosure, but are to be accorded the fullscope consistent with the language of the claims. All structural andfunctional equivalents to the various components of the exemplaryembodiments described throughout this disclosure that are known or latercome to be known to those of ordinary skill in the art are expresslyincorporated herein by reference and are intended to be encompassed bythe claims. Moreover, nothing disclosed herein is intended to bededicated to the public regardless of whether such disclosure isexplicitly recited in the claims. No claim element is to be construedunder the provisions of 35 U.S.C. §112(f) unless the element isexpressly recited using the phrase “means for” or, in the case of amethod claim, the element is recited using the phrase “step for.”

What is claimed is:
 1. A mobile apparatus, comprising: an image sensorconfigured to convert an optical image into an electrical signal, theoptical image including an image of a vehicle license plate; a licenseplate detector configured to: process, by a processor, the electricalsignal to identify one or more object images from the image, each ofsaid one or more images comprising a candidate vehicle license plateimage, process, by the processor, the electrical signal to crop theimage to said identified one or more of the object images, process, bythe processor, the electrical signal to score each of said cropped oneor more of the object images based on a probability that the croppedobject image comprises the vehicle license plate image, wherein each ofthe cropped one or more object images is scored by: detecting aplurality of alphanumeric characters in the object image, and applying ascore based on a position of one of the plurality of characters relativeto a position of another one of the plurality of characters, and basedon said scored object images, process, by the processor the electricalsignal to recover information from the vehicle license plate image; andan interface configured to transmit the vehicle license plateinformation to a remote apparatus and receive verification of vehicleownership in response to the transmission.
 2. The mobile apparatus ofclaim 1, wherein the license plate detector is further configured toprocess the electrical signal to identify one or more object images fromthe optical image, each of said one or more of the object imagescomprising a candidate vehicle license plate image.
 3. The mobileapparatus of claim 2, wherein the license plate detector is furtherconfigured to process the electrical signal to dewarp at least one ofsaid one or more of the object images.
 4. The mobile apparatus of claim2, further comprising a display and a rendering module configured torender the optical image to the display and overlay a detectionindicator on each of said identified one or more of the object images inthe optical image.
 5. The mobile apparatus of claim 1, wherein thelicense plate detector is further configured to recover the informationfrom a portion of the electrical signal corresponding to the selectedone of said identified one or more of the object images.
 6. The mobileapparatus of claim 1, further comprising an image filter configured to:apply a set of filter parameters to the electrical signal; anddynamically adjust the parameters based on at least one of colortemperature, ambient light, object image location, and the location ofthe apparatus.
 7. A mobile apparatus, comprising: an image sensorconfigured to convert an optical image into an electrical signal,wherein the optical image comprises a plurality of object images, andwherein one of the plurality of object images comprises an image of avehicle license plate; a license plate detector configured to: process,by a processor, the electrical signal to identify one or more objectimages from the image, each of said one or more images comprising acandidate vehicle license plate image, process, by the processor, theelectrical signal to crop the image to said identified one or more ofthe object images, process, by the processor, the electrical signal toscore each of said cropped one or more of the object images based on aprobability that the cropped object image comprises the vehicle licenseplate image, wherein each of the cropped one or more object images isscored by: detecting a plurality of alphanumeric characters in theobject image, and applying a score based on a position of one of theplurality of characters relative to a position of another one of theplurality of characters, and based on said scored object images,process, by the processor the electrical signal to recover informationfrom the vehicle license plate image from a portion of the electricalsignal corresponding to said one of the plurality of object images; andan interface configured to transmit the vehicle license plateinformation to a remote apparatus and receive verification of vehicleownership in response to the transmission.
 8. The mobile apparatus ofclaim 7, wherein the license plate detector is further configured toprocess the electrical signal to dewarp said one of the object imagesprior to recovering the information.
 9. The mobile apparatus of claim 7,further comprising a display and a rendering module configured to renderthe optical image to the display and overlay a detection indicator oneach of the plurality of object images.
 10. The mobile apparatus ofclaim 9, wherein the rendering module is further configured to overlay afirst detection indicator over said one of the plurality of objectimages and a second detection indicator over the other object images ofsaid plurality of object images.
 11. The mobile apparatus of claim 10,wherein the rendering module is further configured to cause the firstdetection indicator to appear visually different from the seconddetection indicator on the display.
 12. The mobile apparatus of claim 7,wherein the license plate detector is further configured to process theelectrical signal to score each of the plurality of object images basedon a probability that the each of the plurality of object imagescomprises the vehicle license plate image.
 13. The mobile apparatus ofclaim 12, wherein the license plate detector is further configured toselect said one of the plurality of object images based on the selectedobject image's score.
 14. A mobile apparatus, comprising: an imagesensor configured to convert an optical image into an electrical signal,the optical image comprising an image of a vehicle license plate; adisplay; a rendering module configured to render the optical image tothe display; an image filter configured to apply one or more filterparameters to the electrical signal based on at least one of colortemperature of the image, ambient light, and motion of the apparatus;and a license plate detector configured to: process, by a processor, theelectrical signal to identify one or more object images from the image,each of said one or more images comprising a candidate vehicle licenseplate image, process, by the processor, the electrical signal to cropthe image to said identified one or more of the object images, process,by the processor, the electrical signal to score each of said croppedone or more of the object images based on a probability that the croppedobject image comprises the vehicle license plate image, wherein each ofthe cropped one or more object images is scored by: detecting aplurality of alphanumeric characters in the object image, and applying ascore based on a position of one of the plurality of characters relativeto a position of another one of the plurality of characters, and basedon said scored object images, process, by the processor the electricalsignal to recover information from the vehicle license plate image;wherein the rendering module is further configured to overlay adetection indicator on the displayed optical image to assist the userposition of the apparatus with respect to the optical image in responseto a signal from the image filter; and wherein the rendering module isfurther configured to provide an alert to the display when the licenseplate detector fails to recover the vehicle license plate information;and an interface configured to transmit the vehicle license plateinformation to a remote apparatus and receive verification of vehicleownership in response to the transmission.
 15. The mobile apparatus ofclaim 14, wherein the optical image comprises a plurality of objectimages, and wherein the license plate detector is further configured toprocess the electrical signal to identify one or more of the pluralityof object images from the image, each of said one or more of theplurality of object images comprising a candidate vehicle license plateimage.
 16. The mobile apparatus of claim 15, wherein the license platedetector is further configured to process the electrical signal todewarp at least one of said one or more of the plurality of objectimages.
 17. A computer program product for a mobile apparatus having animage sensor configured to convert an optical image into an electricalsignal, the optical image including a plurality of object images,wherein one of the object images comprises an image of a vehicle licenseplate, the computer program product comprising: a non-transitory machinereadable medium comprising code to: process the electrical signal toselect said one of the object images; process the electrical signal toidentify one or more object images from the image, each of said one ormore images comprising a candidate vehicle license plate image, processthe electrical signal to crop the image to said identified one or moreof the object images, process the electrical signal to score each ofsaid cropped one or more of the object images based on a probabilitythat the cropped object image comprises the vehicle license plate image,wherein each of the cropped one or more object images is scored by:detecting a plurality of alphanumeric characters in the object image,and applying a score based on a position of one of the plurality ofcharacters relative to a position of another one of the plurality ofcharacters, and based on said scored object images, process a portion ofthe electrical signal corresponding to the selected said one of theobject images to recover information from the vehicle license plateimage; transmit the vehicle license plate information to a remoteapparatus; and receive verification of vehicle ownership in response tothe transmission.
 18. The computer program product of claim 17, whereinthe code to process the electrical signal is configured to process theelectrical signal to identify one or more of the object images from theimage, each of said one or more of the object images comprising acandidate vehicle license plate image.
 19. The computer program productof claim 18, wherein the code for processing the electrical signal isfurther configured to dewarp at least one of said one or more of theobject images.
 20. The computer program product of claim 17, wherein thecode to process the electrical signal is further configured to selectsaid one of the object images from said identified one or more of theobject images based on the scores.
 21. The computer program product ofclaim 20, wherein the code to process the electrical signal is furtherconfigured to recover the information from a portion of the electricalsignal corresponding to the selected one of said identified one or moreof the object images.
 22. A mobile apparatus, comprising: an imagesensor configured to convert an optical image into an electrical signal,the optical image including an image of a vehicle license plate; atiming circuit configured to sample the electrical signal at a framerate; a license plate detector configured to: process, by a processor,the electrical signal to identify one or more object images from theimage, each of said one or more images comprising a candidate vehiclelicense plate image, process, by the processor, the electrical signal tocrop the image to said identified one or more of the object images,process, by the processor, the electrical signal to score each of saidcropped one or more of the object images based on a probability that thecropped object image comprises the vehicle license plate image, whereineach of the cropped one or more object images is scored by: detecting aplurality of alphanumeric characters in the object image, and applying ascore based on a position of one of the plurality of characters relativeto a position of another one of the plurality of characters, and basedon said scored object images, process, by the processor the sampledelectrical signal to recover information from the vehicle license plateimage; and an interface configured to transmit the vehicle license plateinformation to a remote apparatus and receive verification of vehicleownership in response to the transmission.
 23. The apparatus of claim22, wherein the sampled electrical signal comprises a plurality ofsamples, and wherein the license plate detector is further configured toidentify one or more object images from at least one of the plurality ofsamples.
 24. The apparatus of claim 23, wherein at least one of said oneor more of the object images comprising a candidate vehicle licenseplate image.
 25. The apparatus of claim 24, wherein the license platedetector is further configured to process said at least one of theplurality of samples to score each of said one or more of the objectimages based on a probability that the object image comprises thevehicle license plate image.
 26. The apparatus of claim 25, wherein thelicense plate detector is further configured to process at least firstand second samples of the plurality of samples to score each of said oneor more of the object images from each of said at least first and secondsamples.
 27. The apparatus of claim 26, wherein each of said one or moreobject images detected in the first sample is associated with locationinformation, and wherein the license plate detector is furtherconfigured to use the location information to estimate the location ofsaid one or more of the objects in the second sample.
 28. The apparatusof claim 25, wherein the license plate detector is further configured tostop processing the sampled electrical signal when said score is above athreshold value.
 29. The apparatus of claim 23, further comprising adisplay, wherein each sample represents a frame of a video imagepresented to the display; and a rendering module configured to rendereach frame of the video image to the display and overlay a detectionindicator on each of said identified one or more of the object images.30. The apparatus of claim 29, wherein the overlay provides a visualindication of the object image associated with the score that is above athreshold value.